Document-level relation extraction (DocRE) aims to extract relations between entities in a document. While previous research has primarily focused on traditional small models, recent studies have extended the scope to large language models (LLMs). Current LLM-based methods typically focus on filtering all potential relations (candidate relations) within a document at one time and then performing triplet fact extraction. However, most approaches for candidate relation filtering are based on the document level, which results in insufficient correlation between candidate relations and entity pairs. In addition, the data imbalance problem caused by a large amount of no-relation data (NA problem) is another important reason for the suboptimal performance of LLM-based methods. To address these issues, we propose an entity pair-guided relation summarization and retrieval model (EP-RSR) for DocRE, which introduces an innovative LLM-based document-level relation extraction paradigm, EPRF (Entity Pair-Relation-Fact), along with an entity pair-level candidate relation filtering method. Our approach first selects entity pairs that potentially contain relations and uses them to guide relation summarization and retrieval for extracting relation facts. This enhances the relevance between candidate relations and entity pairs while alleviating the issue of imbalanced NA data. Benchmark testing on three datasets demonstrates that our approach achieves state-of-the-art (SOTA) performance for LLM-based models. Our code is available at https://github.com/LookingYu/EP-RSR.

Document-level relation extraction (DocRE) provides a broad context for extracting one or more relations for each entity pair. Large language models (LLMs) have made great progress in relation extraction tasks. However, one of the main challenges we face is that LLMs have difficulty in multi-label relation prediction tasks. Additionally, another noteworthy challenge and discovery we reveal: the small language models (SLMs) for DocRE tend to classify existing relations as ”no relation” (NA), while LLMs tend to predict existing relations for all entity pairs. To address these challenges, we propose a novel method that utilizes LLMs as a refiner, employing task distribution and probability fusion. The task distribution we carefully designed aims to distinguish hard and easy tasks, and feed hard tasks to our LLMs-based framework to reevaluate and refine. Further, in order to effectively solve the multi-label relation prediction problem in the refinement process, we propose a probability fusion method, ensuring and enhancing fusion predictions by maintaining a balance between SLMs and LLMs. Extensive experiments on widely-used datasets demonstrate that our method outperforms existing LLMbased methods without fine-tuning by an average of 25.2% F1. Refining SLMs using our method consistently boosts the performance of the SLMs, achieving new state-of-the-art results compared to existing SLMs and LLMs. Our code: https://github.com/Drasick/Drell.